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<h1>Country Ruggedness and Geographical Data</h1>
<div id="content">
  <h2>Data from 'Ruggedness: The blessing of bad geography in Africa'</h2>
  <h4>by Nathan Nunn and Diego Puga</h4>
  <p>This file documents the dataset of terrain ruggedness
    and other geographical characteristics of countries created by Nathan
    Nunn and Diego Puga for their article <b>'Ruggedness: The blessing of
    bad geography in Africa'</b>, published in the <i><b>Review of Economics and Statistics</b></i> 94(1), February 2012: 20-36, as well as other variables and computer
    code required to reproduce their results. Users of this dataset are asked
    to cite the <i>Review of Economics and Statistics</i> article as the
    source and email <a href="mailto:diego.puga@imdea.org">Diego Puga</a> (<a href="mailto:diego.puga@imdea.org"><code>diego.puga@imdea.org</code></a>)
    the details of any publication in which they use the data.</p>
  <p>There are two main components in this dataset:</p>
  <ul>
    <li>The <a href="#country">country-level
      data</a> on terrain ruggedness and other characteristics of countries
      required to run the regressions in the article 'Ruggedness: The
      blessing of bad geography in Africa'. These data, documented <a href="#countryt">below</a>,
      are available for download as a set of three files:
      <ul>
        <li>The country-level data in Stata version 10/11 format: <code>rugged_data.dta</code>.</li>
        <li>A Stata do file that re-creates the regression tables contained
        in the article 'Ruggedness: The blessing of bad geography in Africa': <code>rugged_regr.do</code>.</li>
        <li>A Stata log file produced when running the corresponding do file: <code>rugged_regr.log</code></li>
      </ul>
    </li>
    <li><a href="#grid">The underlying grid-cell-level
      data on terrain ruggedness</a>. These are calculated at the level
      of 30 arc-second cells on a regular geographic grid covering the
      Earth, and can also be downloaded from this site by following the
      links provided below.</li>
  </ul>
  <a name="country" id="country"></a>
  <h3>Country-Level Data</h3>
  <p>The country-level data on terrain ruggedness and other characteristics
    of countries includes the following variables:</p>
  <ul>
    <li><strong>isocode: Country 3-letter ISO code.</strong> Alpha-3 code
      definitions from the ISO 3166 Maintenance Agency as of 2000.</li>
    <li><strong>isonum: Country numeric ISO code</strong>. Numeric-3 code
      definitions from the ISO 3166 Maintenance Agency as of 2000.</li>
    <li><strong>country: Country name</strong>. English full name definitions
      from the ISO 3166 Maintenance Agency as of 2000.</li>
    <li><strong>rugged: Ruggedness (Terrain Ruggedness Index, 100 m).</strong> This
      is the Terrain Ruggedness Index originally devised by Riley, DeGloria,
      and Elliot (1999) to quantify topographic heterogeneity in wildlife
      habitats providing concealment for preys and lookout posts. Let <i>e<sub>r,c</sub></i> denote
      elevation at the point located in row <i>r</i> and column <i>c</i> of
      a grid of elevation points. Then the Terrain Ruggedness Index of Riley <i>et
      al.</i> (1999) at that point is calculated as &sum;<sub><i>i</i>=<i>r-1</i></sub><sup style="margin-left: -4ex"><i>i</i>=<i>r+1</i></sup>&sum;<sub><i>j</i>=<i>c-1</i></sub><sup style="margin-left: -4ex"><i>j</i>=<i>c+1</i></sup>&nbsp;(<i>e<sub>i,j</sub></i>&nbsp;-&nbsp;<i>e<sub>r,c</sub></i>)<sup>2</sup>.
      The source of elevation data is GTOPO30 (US Geological Survey, 1996),
      a global elevation data set developed through a collaborative international
      effort led by staff at the US Geological Survey�s Center for Earth
      Resources Observation and Science (EROS). Elevations in GTOPO30 are
      regularly spaced at 30 arc-seconds across the entire surface of the
      Earth on a map using a geographic projection, so the sea-level surface
      distance between two adjacent grid points on a meridian is half a nautical
      mile or, equivalently, 926 metres. After calculating the Terrain Ruggedness
      Index for each point on the grid, we average across all grid cells
      in the country not covered by water to obtain the average terrain ruggedness
      of the country's land area. Since the sea-level surface that corresponds
      to a 30 by 30 arcsecond cell varies in proportion to the cosine of
      its latitude, when calculating the average terrain ruggedness &mdash; or
      the average of any other variable &mdash; for each country, we weigh
      each cell by its latitude-varying sea-level surface. We assign land
      to countries &mdash; for this and other variables &mdash; using digital
      boundary data based on the fifth edition of the Digital Chart of the
      World (US National Imagery and Mapping Agency, 2000), which we have
      updated to reflect 2000 country boundaries using information from the
      International Organization for Standardization ISO 3166 Maintenance
      Agency and other sources. We exclude areas covered by permanent inland
      water area features contained in the same edition of the Digital Chart
      of the World. The units for the terrain ruggedness index correspond
      to the units used to measure elevation differences. In our calculation,
      ruggedness is measured in hundreds of metres of elevation difference
      for grid points 30 arc-seconds (926 metres on the equator or any meridian)
      apart.</li>
    <li><strong>rugged_popw: Alternative ruggedness (pop. weighted TRI, 100
      m)</strong>. In addition to the Terrain Ruggedness Index, we provide
      four alternative ruggedness measures. To capture the possibility
      that ruggedness may be more important (and thus should be given more
      weight) in areas that are more densely populated today, we calculate
      a population-weighted measure of ruggedness. We start by calculating
      the Terrain Ruggedness Index of each 30 by 30 arc-second cell but,
      in averaging this for each country, we weight ruggedness in each
      cell by the share of the country�s population located in that cell.
      The population data are for 2000 and are from the LandScan data set
      (Oak Ridge National Laboratory, 2001), which has the same 30 arc-second
      resolution as GTOPO30. Units are hundreds of metres.</li>
    <li><strong>rugged_slope: Alternative ruggedness (average slope, %)</strong>.
      As another alternative ruggedness measure, using the same GTOPO30 elevation
      data, we calculate the average uphill slope of the country&rsquo;s
      surface area. To do this, for each point on the elevation grid, we
      calculate the absolute value of the difference in elevation between
      this point and the point on the Earth�s surface 30 arc-seconds North
      of it, and then divides this by the sea-level distance between the
      two points to obtain the uphill slope. The same calculation is performed
      for each of the eight major directions of the compass (North, Northeast,
      East, Southeast, South, Southwest, West, and Northwest), and the eight
      slopes obtained are then averaged to calculate the mean uphill slope
      for the 30 by 30 arc-second cell centred on the point. Finally, we
      average across all grid cells in the country not covered by water (taking
      into account the latitude-varying sea-level surface that corresponds
      to the 30 by 30 arc-second cell centred on each point) to obtain the
      average uphill slope of the country�s land area. </li>
    <li><strong>rugged_lsd: Alternative ruggedness (local std. deviation
      in elevation, 100 m)</strong>. Another alternative ruggedness measure
      is the average standard deviation of elevation within the same eight-cell
      neighbourhood. Units are hundreds of metres.</li>
    <li><strong>rugged_pc: Alternative ruggedness (% moderately to highly
      rugged)</strong>. This alternative ruggedness measure is motivated
      by the possibility that what matters is having a large-enough amount
      of sufficiently-rugged terrain nearby, even if some portions of the
      country are fairly flat. To capture this logic, we calculate the
      percentage of a country�s land area that is highly rugged. We use
      a threshold set at 240 metres for the Terrain Ruggedness Index calculated
      on the 30 arc-seconds grid, below which Riley <i>et al.</i>, 1999,
      classify terrain as being �level� to �intermediately rugged�.</li>
    <li><strong>land_area: Land area (1000 Ha)</strong>. The source is the
      Food and Agriculture Organization (2008), except for Macau and Hong
      Kong where it is the Encyclop&aelig;dia Britannica. Units are thousands
      of hectares.</li>
    <li><strong>lat: Latitude</strong>. Expressed in decimal degress, for
      the geographical centroid of the country.</li>
    <li><strong>lon: Longitude</strong>. Expressed in decimal degress, for
      the geographical centroid of the country.</li>
    <li><strong>soil: % Fertile soil</strong>. On the basis of the FAO/UNESCO
      Digital Soil Map of the World and linked soil association composition
      table and climatic data compiled by the Climate Research Unit of the
      University of East Anglia, Fischer, van Velthuizen, Shah, and Nachtergaele
      (2002) identify whether each cell on a 5-minute grid covering almost
      the entire land area of the Earth is subject to various constraints
      for growing rainfed crops. Based on plates 20 (soil moisture storage
      capacity constraints), 21 (soil depth constraints), 22 (soil fertility
      constraints), 23 (soil drainage constraints), 24 (soil texture constraints),
      and 25 (soil chemical constraints) in Fischer <i>et al.</i> (2002)
      and the country boundaries described above, we calculate the percentage
      of the land surface area of each country that has fertile soil (defined
      as soil that is not subject to severe constraints for growing rainfed
      crops in terms of either soil fertility, depth, chemical and drainage
      properties, or moisture storage capacity). Cape Verde, French Polynesia,
      Mauritius and Seychelles are not covered by the Fischer <i>et al.</i> (2002)
      data, so for these countries we use instead the percentage of their
      land surface area that is classified by the Food and Agriculture Organization
      (2008) as arable land or permanent crop land.</li>
    <li><strong>desert: % Desert</strong>. The percentage of the land surface
      area of each country covered by sandy desert, dunes, rocky or lava
      flows, was calculated on the basis of the desert layer of the <i>Collins
      Bartholomew World Premium</i> digital map data (Collins Bartholomew,
      2005) and the country boundaries described above. This was initially
      computed as a cruder measure of soil (in)fertility for an early draft
      of the paper and is no longer used in the final version. We have left
      it in the dataset in case it is of use to other researchers.</li>
    <li><strong>tropical: % Tropical climate</strong>. Using detailed temperature
      and precipitation data from the Climatic Research Unit of the University
      of East Anglia and the Global Precipitation Climatology Centre of the
      German Weather Service, Kottek, Grieser, Beck, Rudolf, and Rubel (2006)
      classify each cell on a 30 arc-minute grid covering the entire land
      area of the Earth into one of 31 climates in the widely-used K�ppen-Geiger
      climate classification. Based on these data and the country boundaries
      described above, we calculate the percentage of the land surface area
      of each country that has any of the four K�ppen-Geiger tropical climates.</li>
    <li><strong>dist_coast: Average distance to nearest ice-free coast (1000
      km)</strong>. To calculate the average distance to the closest ice-free
      coast in each country, we first compute the distance to the nearest
      ice-free coast for every point in the country in equi-rectangular
      projection with standard parallels at 30 degrees, on the basis of
      sea and sea ice area features contained in the fifth edition of the
      Digital Chart of the World (US National Imagery and Mapping Agency,
      2000) and the country boundaries described above. We then average
      this distance across all land in each country not covered by inland
      water features. Units are thousands of kilometres.</li>
    <li><strong>near_coast: % Within 100 km of ice-free coast</strong>. On
      the basis of the same data used to calculate the average distance to
      nearest ice-free coast, we calculate the percentage of the land surface
      area of each country that is within 100km of the nearest ice-free coast.</li>
    <li><strong>gemstones: Gem diamond extraction 1958-2000 (1000 carats)</strong>.
      Data on gem-quality diamond extracted by each country between 1958-2000
      are obtained from the 1959-2004 editions of the Mineral Yearbook, published
      first by the US Bureau of Mines (US Bureau of Mines, 1960-1996) and
      then by the US Geological Survey (US Geological Survey, 1997�2007).
      We use the most recent data for each country-year in Volume I (Metals
      and Minerals), completed with data from Volume III (Area Reports: International)
      of the 1997�2000 editions. For countries that have split or changed
      boundaries, we assign diamond extraction on the basis of mine location
      with respect to current boundaries. Units are thousands of carats.</li>
    <li><strong>rgdppc_2000: Real GDP per person 2000 -- World Bank</strong>.
      We measure average country-level income by the natural logarithm of
      real gross domestic product per person in 2000. The data are from the
      World Bank World Development Indicators (World Bank, 2006). Units are
      2006 international dollars, with purchasing power parity conversions
      performed using the Elteto-Koves-Szulc method.</li>
    <li><strong>rgdppc_1950_m: Real GDP per person 1950 -- Maddison</strong>.
      To check the robustness of our results to the use of income data from
      other time periods and from an alternative source, in the text we refer
      to results using the natural logarithm of real gross domestic product
      per person in 1950 and in 2000, and its annual average from 1950�2000,
      with data from Angus Maddison (Maddison, 2007, updated October 2008).
      Units are 1990 international dollars, with purchasing power parity
      conversions performed using the Geary-Khamis method.</li>
    <li><strong>rgdppc_1975_m: Real GDP per person 1975 -- Maddison</strong>.</li>
    <li><strong>rgdppc_2000_m: Real GDP per person 2000 -- Maddison</strong>.</li>
    <li><strong>rgdppc_1950_2000_m: Real GDP per person 1950-2000 Average
      -- Maddison</strong>.</li>
    <li><strong>q_rule_law: Rule of law 1996-2000</strong>. To measure the
      quality of governance in each country, we use the composite variable �rule
      of law�
      from version VII of the World Bank�s Worldwide Governance Indicators
      database (Kaufmann, Kraay, and Mastruzzi, 2008). It consists of &ldquo;perceptions
      of the extent to which agents have confidence in and abide by the rules
      of society, and in particular the quality of contract enforcement,
      property rights, the police, and the courts, as well as the likelihood
      of crime and violence&rdquo; (Kaufmann <i>et al.</i>, 2008, p. 7).</li>
    <li><strong>cont_africa: Continent indicator: Africa</strong>. Continent
      indicators follow the definitions of the United Nations Statistics
      Division as of 2000.</li>
    <li><strong>cont_asia: Continent indicator: Asia</strong></li>
    <li><strong>cont_europe: Continent indicator: Europe</strong></li>
    <li><strong>cont_oceania: Continent indicator: Oceania</strong></li>
    <li><strong>cont_north_america: Continent indicator: North America</strong></li>
    <li><strong>cont_south_america: Continent indicator: South America</strong></li>
    <li><strong>legor_gbr: Legal origin indicator: Common law</strong>. Legal
      origin indicators are from La Porta, Lopez-de-Silanes, Shleifer, and
      Vishny (1999). Some of our regressions include French Polynesia, absent
      from their data, which we have coded as French civil law.</li>
    <li><strong>legor_fra: Legal origin indicator: French civil law</strong>.</li>
    <li><strong>legor_soc: Legal origin indicator: Socialist law</strong>.</li>
    <li><strong>legor_deu: Legal origin indicator: German civil law</strong>.</li>
    <li><strong>legor_sca: Legal origin indicator: Scandinavian law</strong>.</li>
    <li><strong>colony_esp: Colonial origin indicator: Spanish</strong>.
      European colonial origin indicators are based on Teorell and Hadenius
      (2007). They distinguish between British, French, Portuguese, Spanish,
      and other European (Dutch, Belgian and Italian) colonial origin for
      countries colonized since 1700. For countries under several colonial
      powers, the last one is counted provided that it lasted for 10 years
      or longer. Since Teorell and Hadenius (2007) exclude the British settler
      colonies (the United States, Canada, Australia, Israel and New Zealand),
      we code theses as having a British colonial origin. We complete their
      data using the same rule to determine the European colonial origin
      of French Polynesia (French), Hong Kong (British), Macau (Portuguese),
      New Caledonia (French), Nauru (British), Philippines (Spanish), Puerto
      Rico (Spanish), and Papua New Guinea (British).</li>
    <li><strong>colony_gbr: Colonial origin indicator: British</strong>.</li>
    <li><strong>colony_fra: Colonial origin indicator: French</strong>.</li>
    <li><strong>colony_prt: Colonial origin indicator: Portuguese</strong>.</li>
    <li><strong>colony_oeu: Colonial origin indicator: Other European</strong>.</li>
    <li><strong>africa_region_n: African region indicator: North</strong>.
      Region indicators for Sub-Saharan Africa (East Africa, Central Africa,
      West Africa, and South Africa) are from Bratton and van deWalle (1997).
      We assign African countries North of the Saharan desert, which were
      not classified by Bratton and van deWalle (1997), to the region of
      North Africa.</li>
    <li><strong>africa_region_s: African region indicator: South</strong>.</li>
    <li><strong>africa_region_w: African region indicator: West</strong>.</li>
    <li><strong>africa_region_e: African region indicator: East</strong>.</li>
    <li><strong>africa_region_c: African region indicator: Central</strong>.</li>
    <li><strong>slave_exports: Slave exports 1400-1900</strong>. Estimates
      of the number of slaves exported between 1400 and 1900 in Africa's
      four slave trades are from Nunn (2008). The data are constructed by
      combining shipping data with data from various historic documents reporting
      the ethnicities of slaves shipped from Africa. Combining the two sources,
      Nunn is able to construct an estimate of the number of slaves shipped
      from each country in Africa between 1400 and 1900 during Africa�s four
      slave trades. See Nunn (2008) for more information on the nature of
      the data. Units are number of people.</li>
    <li><strong>dist_slavemkt_atlantic: Distance to slave markets, Atlantic
      trade (1000 km)</strong>. The four variables measuring the distance
      from each country to the closest final destination slave market in
      each of Africa's four slave trades are takenfrom Nunn (2008). For
      the trans-Atlantic and Indian Ocean slave trades, the measure is
      the sailing distance from the point on the coast that is closest
      to the country�s centroid to the closest final export destination
      for slave trade. For the trans-Saharan and Red Sea slave trades,
      the measure is the great-circle overland distance from the country�s
      centroid to the closest final export destination for that slave trade.
      Units are thousands of kilometres.</li>
    <li><strong>dist_slavemkt_indian: Distance to slave markets, Indian trade
      (1000 km)</strong>. </li>
    <li><strong>dist_slavemkt_saharan: Distance to slave markets, Saharan
      trade (1000 km)</strong>. </li>
    <li><strong>dist_slavemkt_redsea: Distance to slave markets, Red Sea
      trade (1000 km)</strong> .</li>
    <li><strong>pop_1400: Population 1400</strong>. The data are constructed
      using historic population estimates from McEvedy and Jones (1978).
      For countries grouped with others in McEvedy and Jones (1978), we allocate
      population to countries in the group according to the distribution
      of population in 1950, obtained from United Nations (2007). Units are
      number of people.</li>
    <li><strong>european_descent: % European descent</strong>. The variable,
      calculated from version 1.1 of the migration matrix of Putterman and
      Weil (2010), estimates the percentage of the year 2000 population in
      every country that is descended from people who resided in Europe in
      1500. This variable was used to perform an additional robustness check
      in a draft of the paper and is no longer used in the final version.
      We have left it in the dataset in case it is of use to other researchers.</li>
  </ul>
  <a name="grid" id="grid"></a>
  <h3>Grid-cell-level data on terrain ruggedness</h3>
  <p>Researchers interested in using the terrain ruggedness variables at
    the level of countries will find these included in the country-level
    data described above. For those interested in using the terrain ruggedness
    variables for different geographic units, we also provide the underlying
    data at the level of individual cells on a 30 arc-seconds grid across
    the surface of the Earth. Three grid files are available:</p>
  <ul>
    <li>Terrain Ruggedness Index, in milimetres (see below for an explanation regarding the units): available as <code>tri.txt</code>,
      an <acronym>ASCII</acronym> grid compressed in the zip file <code>tri.zip</code> (616,110&nbsp;Kb.)      .</li>
    <li>Average slope, as an alternative ruggedness measure, in thousandths
      of a percentage point: available as <code>slope.txt</code>, an <acronym>ASCII</acronym> grid
      compressed in the zip file <code>slope.zip</code> (468,984&nbsp;Kb.)      .</li>
    <li>The surface area of each cell, in square metres, which must be used
      to weight the ruggedness measures when averaging across areas: available
      as <code>cellarea.txt</code>, an <acronym>ASCII</acronym> grid compressed
      in the zip file <code>cellarea.zip</code> (9,298&nbsp;Kb.)      .</li>
  </ul>
  <p>To use these <acronym>ASCII</acronym> grids in ArcGIS, after unzipping
    each downloaded file, you will need to convert it into a binary grid.
    You can do this through point-and-click by using the Arc Toolbox and,
    within Conversion Tools, selecting To Raster, and then <acronym>ASCII</acronym> to
    Raster. As input <acronym>ASCII</acronym> file, specify the text file
    you unzipped (e.g., <code>tri.txt</code>) and make sure Integer is selected
    as Output Data Type (at the moment of writing, ArcGIS is still a 32-bit
    application and a grid covering the Earth with 30 arc-seconds resolution
    is too large to be handled when values are stored as floating point values
    instead of integers). Alternatively, at the ArcInfo command line, one
    can use the ArcInfo Grid command <code>asciigrid</code> (e.g., <code>tri=asciigrid(tri.txt,INT)</code>).</p>
  <p>When averaging the Terrain Ruggedness Index or average slope over areas,
    it is important to take into account that the sea-level surface that
    corresponds to a 30 by 30 arcsecond cell varies in proportion to the
    cosine of its latitude (so it starts at 860 square kilometres at the
    equator and approaches 0 square kilometres as one gets sufficiently close
    to the poles). One should therefore calculate a weighted average, using
    as weights the values of the area of each cell, provided by the grid <code>cellarea.txt</code>.</p>
  <p>Note that the grids are in different units relative to the variables
    in the country-level data used in the regressions. In particular, the
    Terrain Ruggedness Index is in milimetres in the 30 arc-seconds grid
    as opposed to hundreds of metres in the country-level data. This is again
    due to storage constraints imposed by ArcGIS being a 32-bit application.
    After calculating the weighted average for an area, divide values by
    100,000 to obtain the Terrain Ruggedness Index in hundreds of metres.
    Average slope is in thousandths of a percentage point in the 30 arc-seconds
    grid as opposed to percentage points in the country-level data. After
    calculating the weighted average for an area, divide values by 1,000
    to obtain average slope in percent.</p>
  <p>Finally, note that to calculate the country-level averages of the Terrain
    Ruggedness Index or average slope included in the country-level data,
    in addition to weighting cells by their sea-level surface area, we exclude
    any land in each country covered by permanent inland water features.<br />
    &nbsp;</p>
  <h3>References</h3>
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    <dt style="text-indent:-2em">Maddison, Angus. 2007. <i>Contours of the
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    <dt style="text-indent:-2em">Nunn, Nathan. 2008. The long term effects
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  </dl>
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